A gentle Introduction to the TIMi Suite
The TIMi Academy gathers tutorials, training videos and technical resources to help you understand data preparation, predictive analytics and large-scale data processing.
Introduction to the TIMi Suite
Start with the basics
The best way to start learning the TIMi Suite is to follow the introductory training videos dedicated to Anatella and the analytics workflow.
These tutorials cover the essential concepts needed to manipulate and analyze data efficiently.
How to install TIMi/Anatella on a computer?
Your first Anatella data transformation
Let’s use a row-filter box!
Aggregate, sort and plot data
The Anatella cache system
How to put your Anatella graphs in production
Anatella’s best practices
The most common Anatella boxes
Core Data Concepts
Understanding data engineering and analytics
The Academy also explains the fundamental concepts behind modern data infrastructures.
These resources help users understand how analytical systems work and how to design efficient data architectures.
Topics include:
Data Integration (ETL)
Data Warehousing
Reporting & BI
CRM softwares
Each topic explains both the technical concepts and the practical applications in real organizations.
Advanced Analytics
2 different approaches
Analytical CRM can be classified in 2 categories: Analytical CRM tools based on segmentation techniques and Analytical CRM tools based on predictive techniques.
By its very nature, segmentation is a technique well-adapted for exploratory work. In opposition, predictive analytics is discriminatory in nature. Analytical CRM tools based on predictive techniques usually generates a higher ROI for marketing campaigns.
Of the utility of the test dataset
When comparing the accuracy of 2 different predictive models to know which predictive model is the best one (the one with the highest accuracy & the highest ROI), you must compare ONLY the “lift curves computed on the TEST dataset” and if possible, use always the same TEST dataset.
Not all lifts are born equal
When we are constructing a model that makes no mistakes on the “training dataset”, we obtain a model that doesn’t perform well on unseen data.
The “generalization ability” of our model is poor. The predictive model is using some information there were in reality noises. This phenomenon is named “Over-fitting”.
A model that “overfits” the data can have an accuracy of 100% when applied on the learning set.
Get in touch
Stop renting compute power. Start owning your company.
Discuss your constraints, your volumes, your governance requirements.
We will show you how TIMi fits into your architecture.
